Thomas Davenport and his colleagues explore the transformative impact of artificial intelligence (AI) on marketing strategies and consumer behavior. The authors present a framework for understanding AI's current applications and future potential, considering intelligence levels, task types, and robotic integration. The article highlights AI's effectiveness in predictive analytics and personalization, noting its influence across industries such as transportation, sales, and retail. It also emphasizes critical challenges, including privacy, bias in algorithms, and ethical considerations in AI deployment. Ultimately, the research suggests AI should augment human marketers, enabling businesses to harness its power while maintaining a human-centered approach, and recommends future research into AI adoption, consumer trust, and human-AI collaboration.

Jules White and colleagues at Vanderbilt University have created a catalog of prompt patterns to improve interactions with large language models like ChatGPT. These patterns, similar to design patterns in software engineering, offer structured techniques for crafting prompts to obtain predictable and beneficial responses from AI systems. The patterns are organized into six categories: Input Semantics, Output Customization, Error Identification, Prompt Improvement, Interaction, and Context Control. Each pattern includes a definition, example, and use case illustrating its practical application across fields like marketing, education, and software development. By using these patterns, users can enhance AI's capabilities for various tasks, including automation, content generation, problem-solving, and decision-making. Ultimately, the catalog aims to make AI interactions more precise, reliable, and useful through the strategic design of prompts.

This document offers a guide to effective prompt design for marketing using AI. It details the characteristics of a good prompt, including clarity, context, specificity, and tone. The guide then explains how to structure prompts for various AI outputs, such as text, summaries, and visuals, and discusses the balance between open-ended and closed-ended prompts. Finally, it covers managing ambiguity, iterative refinement, contextualization, and the importance of prompt templates, providing numerous examples to illustrate best practices.

DeepSeek, a Chinese AI startup, has rapidly gained prominence by developing cost-effective and efficient AI models, challenging established American tech giants. Its success, particularly with its V3 and R1 models, is attributed to innovative training methods and a partially open-source approach. However, this has sparked controversy regarding the use of knowledge distillation potentially leveraging US technology and raised concerns about intellectual property, ethical implications, and the influence of Chinese government censorship. The situation has prompted a reevaluation of strategies by US companies, including a potential shift towards greater openness, and highlights the evolving dynamics of the global AI market.

This document introduces prompt engineering for marketing, defining it as the strategic crafting of instructions for AI models to generate relevant outputs. It emphasizes key principles like clarity, context, and constraints, illustrating how well-designed prompts improve efficiency and personalization in various marketing applications, from ad copy to social media. The text also explores different prompt types and patterns, advanced techniques like prompt chaining and dynamic prompts, and crucial ethical considerations like bias mitigation and transparency. Finally, it examines the future of prompt engineering, including anticipated advancements in automation and multimodal integration.

This podcast  provides a comprehensive overview of large language models (LLMs), explaining their underlying architecture based on transformer neural networks and their training processes involving pretraining and fine-tuning. It details key components like the self-attention mechanism and tokenization, explores text generation methods, and discusses the importance of prompt engineering for effective interaction. Furthermore, the text addresses the scalability and limitations of LLMs, including bias, accuracy concerns, and environmental impact, while also outlining promising future research directions. Finally, it highlights various applications of LLMs across diverse fields.

This podcasts discusses a widely cited 2021 marketing science journal article proposing a strategic framework for integrating artificial intelligence (AI) into marketing. The framework categorizes AI into three types—mechanical, thinking, and feeling—and applies them across the marketing research, strategy, and action cycle. The podcast discusses how each AI type can enhance each marketing stage, from data collection and analysis to personalized messaging and dynamic pricing. The podcast also addresses challenges like data privacy and algorithmic bias, and suggests areas for future AI research in marketing. Ultimately, it offers an illuminating discussion and  roadmap for businesses to leverage AI for competitive advantage.

In this podcast, AI is introduced in a friendly and thorough manner, bringing to life possibilities for business professionals.